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High Throughput Heuristics for Prioritizing Human Exposure to Environmental Chemicals
Citation:
Wambaugh, J., A. Wang, A. Frame, K. Dionisio, P. Egeghy, R. Judson, AND Woodrow Setzer. High Throughput Heuristics for Prioritizing Human Exposure to Environmental Chemicals. ENVIRONMENTAL SCIENCE & TECHNOLOGY. American Chemical Society, Washington, DC, 48:12760-12767, (2014).
Impact/Purpose:
The methods described by this manuscript provide a highly improved methodology for HTS of human exposure to environmental chemicals. The manuscript includes a ranking of 7785 environmental chemicals with respect to potential human exposure, including most of the Tox21 in vitro hazard HTS library.
Description:
The risk posed to human health by any of the thousands of untested anthropogenic chemicals in our environment is a function of both the potential hazard presented by the chemical, and the possibility of being exposed. Without the capacity to make quantitative, albeit uncertain, forecasts of exposure, the putative risk of adverse health effect from a chemical cannot be evaluated. We used Bayesian methodology to infer ranges of exposure intakes that are consistent with biomarkers of chemical exposures identified in urine samples from the U.S. population by the National Health and Nutrition Examination Survey (NHANES). We perform linear regression on inferred exposure for demographic subsets of NHANES demarked by age, gender, and weight using high throughput chemical descriptors gleaned from databases and chemical structure-based calculators. We find that five of these descriptors are capable of explaining roughly 50% of the variability across chemicals for all the demographic groups examined, including children aged 6-11. For the thousands of chemicals with no other source of information, this approach allows rapid and efficient prediction of average exposure intake of environmental chemicals.
